// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#include "main.h"

template<typename MatrixType>
void
matrixVisitor(const MatrixType& p)
{
	typedef typename MatrixType::Scalar Scalar;

	Index rows = p.rows();
	Index cols = p.cols();

	// construct a random matrix where all coefficients are different
	MatrixType m;
	m = MatrixType::Random(rows, cols);
	for (Index i = 0; i < m.size(); i++)
		for (Index i2 = 0; i2 < i; i2++)
			while (m(i) == m(i2)) // yes, ==
				m(i) = internal::random<Scalar>();

	Scalar minc = Scalar(1000), maxc = Scalar(-1000);
	Index minrow = 0, mincol = 0, maxrow = 0, maxcol = 0;
	for (Index j = 0; j < cols; j++)
		for (Index i = 0; i < rows; i++) {
			if (m(i, j) < minc) {
				minc = m(i, j);
				minrow = i;
				mincol = j;
			}
			if (m(i, j) > maxc) {
				maxc = m(i, j);
				maxrow = i;
				maxcol = j;
			}
		}
	Index eigen_minrow, eigen_mincol, eigen_maxrow, eigen_maxcol;
	Scalar eigen_minc, eigen_maxc;
	eigen_minc = m.minCoeff(&eigen_minrow, &eigen_mincol);
	eigen_maxc = m.maxCoeff(&eigen_maxrow, &eigen_maxcol);
	VERIFY(minrow == eigen_minrow);
	VERIFY(maxrow == eigen_maxrow);
	VERIFY(mincol == eigen_mincol);
	VERIFY(maxcol == eigen_maxcol);
	VERIFY_IS_APPROX(minc, eigen_minc);
	VERIFY_IS_APPROX(maxc, eigen_maxc);
	VERIFY_IS_APPROX(minc, m.minCoeff());
	VERIFY_IS_APPROX(maxc, m.maxCoeff());

	eigen_maxc = (m.adjoint() * m).maxCoeff(&eigen_maxrow, &eigen_maxcol);
	Index maxrow2 = 0, maxcol2 = 0;
	eigen_maxc = (m.adjoint() * m).eval().maxCoeff(&maxrow2, &maxcol2);
	VERIFY(maxrow2 == eigen_maxrow);
	VERIFY(maxcol2 == eigen_maxcol);

	if (!NumTraits<Scalar>::IsInteger && m.size() > 2) {
		// Test NaN propagation by replacing an element with NaN.
		bool stop = false;
		for (Index j = 0; j < cols && !stop; ++j) {
			for (Index i = 0; i < rows && !stop; ++i) {
				if (!(j == mincol && i == minrow) && !(j == maxcol && i == maxrow)) {
					m(i, j) = NumTraits<Scalar>::quiet_NaN();
					stop = true;
					break;
				}
			}
		}

		eigen_minc = m.template minCoeff<PropagateNumbers>(&eigen_minrow, &eigen_mincol);
		eigen_maxc = m.template maxCoeff<PropagateNumbers>(&eigen_maxrow, &eigen_maxcol);
		VERIFY(minrow == eigen_minrow);
		VERIFY(maxrow == eigen_maxrow);
		VERIFY(mincol == eigen_mincol);
		VERIFY(maxcol == eigen_maxcol);
		VERIFY_IS_APPROX(minc, eigen_minc);
		VERIFY_IS_APPROX(maxc, eigen_maxc);
		VERIFY_IS_APPROX(minc, m.template minCoeff<PropagateNumbers>());
		VERIFY_IS_APPROX(maxc, m.template maxCoeff<PropagateNumbers>());

		eigen_minc = m.template minCoeff<PropagateNaN>(&eigen_minrow, &eigen_mincol);
		eigen_maxc = m.template maxCoeff<PropagateNaN>(&eigen_maxrow, &eigen_maxcol);
		VERIFY(minrow != eigen_minrow || mincol != eigen_mincol);
		VERIFY(maxrow != eigen_maxrow || maxcol != eigen_maxcol);
		VERIFY((numext::isnan)(eigen_minc));
		VERIFY((numext::isnan)(eigen_maxc));
	}
}

template<typename VectorType>
void
vectorVisitor(const VectorType& w)
{
	typedef typename VectorType::Scalar Scalar;

	Index size = w.size();

	// construct a random vector where all coefficients are different
	VectorType v;
	v = VectorType::Random(size);
	for (Index i = 0; i < size; i++)
		for (Index i2 = 0; i2 < i; i2++)
			while (v(i) == v(i2)) // yes, ==
				v(i) = internal::random<Scalar>();

	Scalar minc = v(0), maxc = v(0);
	Index minidx = 0, maxidx = 0;
	for (Index i = 0; i < size; i++) {
		if (v(i) < minc) {
			minc = v(i);
			minidx = i;
		}
		if (v(i) > maxc) {
			maxc = v(i);
			maxidx = i;
		}
	}
	Index eigen_minidx, eigen_maxidx;
	Scalar eigen_minc, eigen_maxc;
	eigen_minc = v.minCoeff(&eigen_minidx);
	eigen_maxc = v.maxCoeff(&eigen_maxidx);
	VERIFY(minidx == eigen_minidx);
	VERIFY(maxidx == eigen_maxidx);
	VERIFY_IS_APPROX(minc, eigen_minc);
	VERIFY_IS_APPROX(maxc, eigen_maxc);
	VERIFY_IS_APPROX(minc, v.minCoeff());
	VERIFY_IS_APPROX(maxc, v.maxCoeff());

	Index idx0 = internal::random<Index>(0, size - 1);
	Index idx1 = eigen_minidx;
	Index idx2 = eigen_maxidx;
	VectorType v1(v), v2(v);
	v1(idx0) = v1(idx1);
	v2(idx0) = v2(idx2);
	v1.minCoeff(&eigen_minidx);
	v2.maxCoeff(&eigen_maxidx);
	VERIFY(eigen_minidx == (std::min)(idx0, idx1));
	VERIFY(eigen_maxidx == (std::min)(idx0, idx2));

	if (!NumTraits<Scalar>::IsInteger && size > 2) {
		// Test NaN propagation by replacing an element with NaN.
		for (Index i = 0; i < size; ++i) {
			if (i != minidx && i != maxidx) {
				v(i) = NumTraits<Scalar>::quiet_NaN();
				break;
			}
		}
		eigen_minc = v.template minCoeff<PropagateNumbers>(&eigen_minidx);
		eigen_maxc = v.template maxCoeff<PropagateNumbers>(&eigen_maxidx);
		VERIFY(minidx == eigen_minidx);
		VERIFY(maxidx == eigen_maxidx);
		VERIFY_IS_APPROX(minc, eigen_minc);
		VERIFY_IS_APPROX(maxc, eigen_maxc);
		VERIFY_IS_APPROX(minc, v.template minCoeff<PropagateNumbers>());
		VERIFY_IS_APPROX(maxc, v.template maxCoeff<PropagateNumbers>());

		eigen_minc = v.template minCoeff<PropagateNaN>(&eigen_minidx);
		eigen_maxc = v.template maxCoeff<PropagateNaN>(&eigen_maxidx);
		VERIFY(minidx != eigen_minidx);
		VERIFY(maxidx != eigen_maxidx);
		VERIFY((numext::isnan)(eigen_minc));
		VERIFY((numext::isnan)(eigen_maxc));
	}
}

EIGEN_DECLARE_TEST(visitor)
{
	for (int i = 0; i < g_repeat; i++) {
		CALL_SUBTEST_1(matrixVisitor(Matrix<float, 1, 1>()));
		CALL_SUBTEST_2(matrixVisitor(Matrix2f()));
		CALL_SUBTEST_3(matrixVisitor(Matrix4d()));
		CALL_SUBTEST_4(matrixVisitor(MatrixXd(8, 12)));
		CALL_SUBTEST_5(matrixVisitor(Matrix<double, Dynamic, Dynamic, RowMajor>(20, 20)));
		CALL_SUBTEST_6(matrixVisitor(MatrixXi(8, 12)));
	}
	for (int i = 0; i < g_repeat; i++) {
		CALL_SUBTEST_7(vectorVisitor(Vector4f()));
		CALL_SUBTEST_7(vectorVisitor(Matrix<int, 12, 1>()));
		CALL_SUBTEST_8(vectorVisitor(VectorXd(10)));
		CALL_SUBTEST_9(vectorVisitor(RowVectorXd(10)));
		CALL_SUBTEST_10(vectorVisitor(VectorXf(33)));
	}
}
